Wealth and Investment
Follow the customer along the path of the data. Through a scientific approach you will find the customer in their financial journey and derive meaning by recognizing the unspoken voice that is captured in the data. You will develop and build models that can drive actionable and executable data driven recommendation from the use-cases that is co-created through the strategic themes and build within the context of Bespoke and Personalization’s key focus areas. You will drive the productionisation of ML use-cases within Personalization and will maintain these over the lifecycle of the project. You will constantly strive to enhance and improve the models through thorough and rigorous testing to constantly improve customer experience.
Data Preparation and Insight Extraction
- Directs the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals. Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables/features. Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders
- Designs various mathematical, statistical, and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes. Drives analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business
- Codes, tests and maintains scientific models and algorithms; identifies trends, patterns, and discrepancies in data; and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis.
- Use data profiling and visualisation techniques using tools to understand and explain data characteristics that will inform modelling approaches. Communicate data information to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations.
- Mines data using state-of-the-art methods. Enhances data collection procedures to include information that is relevant for building data models
ML Model development
- Creates, maintains and optimises modelling solutions that enable the forecast of quality data outcomes. Ensures that volumetric predictions are modelled so that resource requirements are optimally considered. Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking. Drives sustainable and effective modelling solutions
- Develops and co-ordinates a comprehensive strategy for productionalising automation software so that it is accurate and well maintained.
- Ensure business integration through integrating model outputs into end-point production systems, where requirements must be understood and adopted relating to data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
- Builds machine learning models from and utilises distributed data processing and analysis methodologies. Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java, SAS E Miner etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.
Stakeholder Engagement and Leadership
- Liaise and collaborate with the Data Science Guild, providing support to the entire department for its data centric needs. Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data science outcomes. Presents findings and observations to team for development of recommendations.
- Acts as a subject matter expert from a data science perspective and provides input into all decisions relating to data science and the use thereof. Educate the organisation on data science perspectives on new approaches, such as testing hypotheses and statistical validation of results. Ensure ongoing knowledge of industry standards as well as best practice and identify gaps between these definitions/data elements and organisation data elements/definitions
Risk, Regulatory and Compliance
- Provides input into Data management and modelling infrastructure requirements and adheres to the organisations’ infrastructure development processes, including the management of User Acceptance Testing (UAT). Conducts regression testing across all relevant systems as required
Preferred Qualification and Experience
- Honours Degree in IT and Computer Sciences / Information Studies
- Post Graduate Degree in the STEM fields, Science, Technology, Engineering or Mathematics
- Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, SAS, PowerBI, Qlikview; Tableau; SSIS, SSRS, Python, JSON , C#, Java, C++, HTML, AWS SageMaker
- Master’s degree in the STEM fields, Science, Technology, Engineering or Mathematics (Prefferred)
- 5- 7 years proven development experience in software and software engineering. Understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles. Project management experience. Exposure to governance and regulatory matters as it related to data. Experience in Building models (Persona Clustering, propensity models, churn, etc.)
- 5 - 7 experience in working with unstructured data (eg. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data. Experience with common data science toolkits, such as SAS, R, Python etc. Experience with data visualization tools, such as PowerBI, Qlikview etc.
- Diagramming and Modelling - Measures proficiency in using the diagramming and modelling techniques vital for requirements analyses.
- Data Integrity - Ability to ensure the accuracy and consistency of data for the duration that the data is stored as well as preventing unintentional alterations or loss of data.
- Research and Information Gathering - Ability to review and study relevant information from various sources to develop new information. Ability to identify primary and secondary authorities to validate the research
- Data Analysis - Ability to analyse statistics and other data, interpret and evaluate results, and create reports and presentations for use by other
- Knowledge Classification - The ability to apply metadata to information to make it easy for other people to find.
- Database Administration - Refers to the knowledge and experience required to manage the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases.